Description

You will architect, fine-tune, and deploy Large Language Models (LLMs) and Generative AI techniques to enhance business applications. You own the end-to-end lifecycle from research to production deployment.

Responsibilities

• Architect and implement RAG, PEFT, and SFT solutions using LLMs like GPT-4 and Llama.
• Build and maintain scalable ML pipelines and GPU-based inference systems on AWS, GCP, or Azure.
• Optimize model performance through prompt engineering, adversarial testing, and efficient inference strategies.
• Translate business requirements into technical specifications and manage project scope and deliverables.
• Guide offshore technical teams and communicate technical trade-offs to non-technical stakeholders.

Required Skills

• 10+ years of experience as an ML Engineer, with 1-2 years dedicated to Generative AI or NLP.
• Strong proficiency in Python and deep learning frameworks (PyTorch or TensorFlow).
• Hands-on experience with LLMs, RAG architectures, LangChain, Vector Databases, and Knowledge Graphs.
• Cloud platform expertise, specifically AWS (S3, Lambda, Glue, SageMaker, Bedrock), GCP, or Azure.
• MLOps and LLM Ops experience with Docker, Kubernetes, and CI/CD tools.
• Experience with IDEs such as Cursor, AWS Kiro, or GitHub Copilot.
• Proven ability to collaborate with product managers, data scientists, and business stakeholders.

Preferred Skills

• Engineering degree in Computer Science or equivalent, with relevant ML certifications.
• Domain experience in banking, financial services, or the payments industry.

Key Skills

Education

Any Graduate